Efficient Rectifier for Wireless Power Transmission Systems
Bibliographic record
Abstract
This article describes a full-bridge rectifier and a receiving antenna array for operation within an innovative wireless power transmission (WPT) system. A high-power transmitter using circularly polarized free-space waves and based on a retrodirective antenna array technology is also employed to boost the overall received RF power at the input of the rectenna. To the best of our knowledge, the proposed rectifier circuit and active antenna configuration are the first demonstration of a high-power beam tracking system for WPT scenarios, being different from previously reported near-field coupling and other lower power harvesting schemes. The main focus of this article is the rectifier design, its bench-top measurements, and operation in such a retrodirective, self-tracking microwave system. A novel approach based on in-phase multitone input signals is also developed to improve rectifier efficiency. The rectifier size is 4.5 cm by 2 cm and can offer more than 86% and 75% RF-to-dc rectification efficiency at 27 dBm for an input signal at 1.7 and 2.4 GHz, respectively. This rectifier circuit component can also be employed in other communication applications or WPT systems, for example, to convert to dc received RF signals or power in the radiating near- and far-field in order to wirelessly charge the batteries of home electronics, such as smartphones, tablets, or Internet of Things (IoT) devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".